AI RESEARCH
SWaRL: Safeguard Code Watermarking via Reinforcement Learning
arXiv CS.LG
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ArXi:2601.02602v2 Announce Type: replace-cross We present SWaRL, a robust and fidelity-preserving watermarking framework designed to protect the intellectual property of code LLMs by embedding unique and verifiable signatures in the generated program. Existing watermarking approaches either rely on handcrafted code transformations or manipulate token generation probabilities at inference time, making them vulnerable to removal attacks or prone to breaking functional correctness. To address these challenges, SWaRL employs a reinforcement learning-based co.